Priority based bandwidth allocation in wireless sensor networks

International Journal of Computer Networks & Communications (IJCNC) Vol.6, No.6, November 2014
PRIORITY BASED BANDWIDTH ALLOCATION IN
WIRELESS SENSOR NETWORKS
Mary Cherian1 and T.R. Gopalakrishnan Nair2
1Department of Computer Science & Engineering, Dr.Ambedkar Institute of Technology,
Bangalore, India
2Dayananda Sagar Institutions, Bangalore, India
ABSTRACT
Most of the sensor network applications need real time communication and the need for deadline aware
real time communication is becoming eminent in these applications. These applications have different dead
line requirements also. The real time applications of wireless sensor networks are bandwidth sensitive and
need higher share of bandwidth for higher priority data to meet the dead line requirements. In this paper
we focus on the MAC layer modifications to meet the real time requirements of different priority data.
Bandwidth partitioning among different priority transmissions is implemented through MAC layer
modifications. The MAC layer implements a queuing model that supports lower transfer rate for lower
priority packets and higher transfer rate for real time packets with higher priority, minimizing the end to
end delay. The performance of the algorithm is evaluated with varying node distribution.
KEYWORDS
Wireless sensor networks, real time, priority of traffic flow, MAC layer, bandwidth allocation
1. INTRODUCTION
A wireless sensor network (WSN) comprises of sensor nodes, which monitor the terrain where
they are deployed and gather the physical environmental parameters which they communicate to
the base station [1]. The sensor nodes are densely deployed in regions where they monitor many
physical phenomena such as vibration, movement of objects, temperature, humidity, pressure,
radiations, noise levels, and light conditions. The sensor nodes self-organize to form an Ad-hoc
network after the deployment. The sensor nodes are resource constraint as these are equipped
with batteries with limited power, tiny microprocessors/microcontrollers, low power transceivers,
and sensors for gathering information about the deployed environment. When a single sensor
node is limited in its capabilities, the composition of large number of nodes offers technological
capabilities. In wireless sensor networks, individual sensor nodes are inherently unreliable and
have very limited capabilities to ensure real-time guarantees. The target is to provide more
reliable services with reduced end-to-end delays, and lower energy consumption in the underlying
sensor network. A hard real-time (HRT) system guarantees that critical tasks complete on time
[2], [3]. Here meeting a deadline is mandatory otherwise system failure occurs which may cause
catastrophic damage [4]. These systems are implemented as safety critical systems such as in
aerospace and defence. Performance of such systems cannot be compensated over any other
feature of the system. A certain amount of latency is allowed for soft-real time solutions. In such
cases the transferred data is not critical and the system used may use the soft real time solutions
for directing the information to the sink. These systems are known as non-safety critical systems
where the system deals with non critical data and use soft real time solutions for data transfer.
DOI : 10.5121/ijcnc.2014.6609 119

The applications of wireless sensor networks spread in different domains viz. military, emergency
situation management, physical world, medical and health, industry, home network and
automotive. Most of the sensor network applications need real time communication and the need
for deadline aware real time communication is becoming eminent in these applications. These
applications have different dead line requirements also. Some of the challenges for real time
communication are random deployment, dynamic network topology, traffic characteristics,
resource constraints, and transient congestion.
In WSN the sensed data from the nodes may travel multiple paths towards the destination. The
available bandwidth in the channel will be portioned, and the response times will become very
lengthy [5]. The real time applications of wireless sensor networks are bandwidth sensitive and
needs higher share of bandwidth for higher priority data to meet the dead line requirements.
Through the assistance from MAC layer, the availability of multiple non-interfering and
prioritized paths can be made available to the routing algorithm. In this paper we focus on the
MAC layer modifications to achieve the real time requirements of the different priority data. In
the layered view of a network model the MAC layer should guarantee the channel access delay.
The network layer routing protocol should bind the end-to-end transmission time [6].
The remainder of the paper is organized as follows. Section 2 aims to provide a survey on the
state of the art of related MAC and routing protocols. Section 3 provides an overview of the
system. The outcomes of the implemented technique are discussed in section 4. Finally, section 5
contains some concluding remarks and the potential research directions.
2. RELATED WORK
In WSN the MAC layer has an important role in deciding the channel access delay which
contributes to the end-to end delay. Contention based protocols [6] can reduce the collisions, but a
deterministic channel access cannot be decided. On the contrary in TDMA based MAC a bounded
and predictable medium delay can be determined, but a central co-ordination is required [6]. The
original IEEE 801.11 [7] uses DCF (Distributed Coordination Function) and PCF (Point
Coordination Function). CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance is
used for the channel access. If channel is free, it waits for DIFS (Distributed inter frame space)
and transmits if still free. If channel is busy, it randomly backs off a number of Slots (minimum
15, maximum 1023). If packet transmission further fails, back-off is increased to a random
window, up to a preconfigured upper limit. But the access method neither differentiates the
different priority data nor reserves the bandwidth. PCF is centrally controlled and uses polling.
The PC (Point Coordinator) maintains the list of nodes eligible for polling. As PCF is not
compatible with all type of real time data, it is not practically used.
The protocols described in [6], S-MAC, T-MAC, B-MAC apply CSMA/CA for medium access,
but they only provide best effort service. 802.11e [8], [9] provides QoS support for wireless
networks at the MAC layer of the protocol stack and enhances the medium access functions. To
achieve hard time data delivery of the packets in addition to a HRT MAC layer, a hard real time
routing protocol is also essential. Taimur Qureshi et al. in [5] present a network Layer based Hard
Real Time Protocol for wireless sensor networks. But the protocol assumes a MAC (Medium
Access Control) layer which is collision free and with, known medium access delay. It also does
not consider the priority of the packets. Other existing real time routing protocols for WSN viz.
RAP [10], SPEED [11], R2TP [12] do not have a priority based data delivery mechanism. They
neither consider a queuing model for packet scheduling at the nodes nor allocate the bandwidth
based on the priority of the packets.
120

We have presented a real time routing protocol, RRDTE in [13] which detects time critical events
in WSN. But the bandwidth allocation is not addressed in this protocol. In [14], we propose a
scheduling algorithm for sensor networks based on the packet service ratio which helps in
congestion control and flow control. But this also does not address the bandwidth partitioning
based on the priority of the packets. M.Caccamo et al., in [15] propose an implicit prioritized
access protocol, for deterministic real time communication which demands prior topological
information and synchronization. However, this is not suitable in non-deterministic environments.
In I-EDF nodes are organized in hexagonal cells. Each cell will have a unique frequency in intra-cellular
communication. The six directions of the hexagon are numbered and the communication
slots alternate with a given direction in inter-cellular communication. The rigid cell-based
organization of the topology has a limitation in real environment where there is random
deployment of sensor nodes.
Watteyne et al. in [16] present a novel hard-real time MAC protocol with realistic assumptions.
The focus is mainly on the MAC layer, as hard real-time needs to be dealt at each layer in
communication architectures. The protocol consists of an initialization phase, followed by a
run-time phase, subdivided in two modes: unprotected and protected mode. Unprotected mode
is used when collision probability is low and multi-hop propagation speed is near optimal. When
collision occurs, the network switches to a slower but collision-free protected mode. But the
protocol works only on linear topologies. This protocol also requires dedicated frequency
channels. DMAC [6], DR-MAC [6] protocols minimize the communication latency, but they are
suitable only for specific tree topologies.
CE. Perkins et al. in [20] presents the AODV, a routing protocol for Ad-Hoc networks, but
priority based data delivery is not addressed in this protocol also. Iftikhar Ahmad et al. in [21]
present a routing protocol by improvising the basic routing mechanism in AODV to assign more
bandwidth to real time traffic (RT), compared to best effort traffic (BE). The protocol reserves a
larger time slot for transmission of RT traffic than that of BE traffic. The protocol assumes only
one queue for the buffering of the packets. Here the BE traffic may be starving and may be
dropped also, if the queue fills up.
3. OVERVIEWOF THE SYSTEM
Sensor nodes share the available resources such as transmission bandwidth, buffer storage and the
processing capability. The delay and loss performance can be quantified as explained in this
subsection. When a packet or connection request arrives at a node, the node may be in a blocking
state because of the unavailability of the resources. The data packets arrive at a node in a random
manner; the time that they spend in the node is also random. Due to lack of resources, the packets
may get blocked or lost. Throughput of the system is the long-term departure rate from the
system. The average arrival rate is given by the inverse of the average inter arrival time. Little’s
formula[17] can be applied to a networking scenario also in which the system can be a network
node, transmission line, multiplexer, a switch or a network itself [18].
121
3.1. Priority queuing at the node
A queuing model is considered at the nodes where requests can belong to one of N priority
classes. When a packet arrives at the system, it joins the queue of its priority class. Each time one
packet is fetched, from the queue, the next packet to be served is selected from the head of the
line of the highest priority non empty queue. It is assumed that the arrival at each priority class is
Poisson with rate in and that the average service time of a class N customers is E [sn]. So the load
offered by class N is, ρn = in E [sn] where ρ is the utilization factor.

122
The highest priority class (class 0) has average waiting time
E [W0] = (in E [s^2]) ∕ (1 – ρ0) (1)
So E [W0] saturates as ρ approaches 1. Thus the saturation point of class 0 is determined only by
its own load. On the other hand, the waiting time for class 1 is given
By
E [W1] = (in E [s ^2]) ∕ (1 – ρ0) (1 – ρ0 – ρ1) (2)
The class 1 queue will saturate when ρ0 + ρ1 approaches 1. Thus the class 1 queue saturation
point is affected by the class 0 load. Similarly, the class N queue saturation point depends on the
sum of the loads of the classes of priority up to N.
At the MAC layer Enhanced Distributed Channel Access (EDCA) method is used. Advantage
here is that this allows ‘tuning’ of the parameters for any specific network/application. Four
priority categories are Class 0, Class 1, Class 2, and Class 3 with Class 0 having the highest
priority. Each Access Category is equipped with a single transmit queue as depicted in Figure 1.
Table 1 Packet Priority Classes
Class Priority Traffic Type
3 Priority 3 Normal- low priority periodic data
2 Priority 2 Control data
1 Priority 1 Real-Time data
0 Priority 0 Real-Time critical data(Lowest Delay)
The MAC layer service data unit (MSDU) with UP (user priorities) which is transferred from the
higher layer (network layer) is mapped, and subsequently placed in the corresponding priority
queue. This ensures bandwidth partitioning among different priority transmissions.

123
MSDU with UP
Mapping of MSDU to Specific Priority
Class 0 Class 1 Class 2 Class3
Figure 1. Queues with Different Priorities
The routing protocol applied is the hard real time protocol for wireless sensor networks (RPS)
which is a multipath routing protocol. Protocol discovers disjoint paths. However, the RPS
algorithm is based on the computation of dynamic routes and can handle the dynamic changes of
the networks. Delay as the metric, a source node selects its route dynamically and checks the
quality of the alternative routes.
4. SIMULATION RESULTS AND DISCUSSIONS
In this work, we consider the bandwidth allocation based on priority of the data for the
applications where sensor nodes are deployed in Ad-hoc manner to detect critical events. We
have applied the discrete event simulator (NS-2) [19] to model the behaviour of the application.
The nature of the events generated in the system is stochastic, discrete, and dynamic.
A single base station gathers data from the sensor nodes. The simulations are conducted with 32,
64,128, and 256 nodes. The wireless sensor nodes are deployed with random topology in terrains
ranging in sizes from 500meter x 500meter to 1500meter x 1500meter. The enhanced 802.11e
EDCA is employed at the MAC layer. Traffic with different priorities is generated by the
application. The bandwidth allocated to each priority data is estimated based on the simulation
results. The results of simulation with NS-2 simulator are charted below.

Figurers 2 to 5 indicate the bandwidth allocations for different priority data for node distributions
for 32, 64, 128, and 256 nodes respectively. Figure 2 depicts the simulation results with the
number of sensor nodes as 256. The Class 0 (Priority 0) traffic with the highest priority (Table 1)
gets the highest share of bandwidth and the Class 3 (Priority 3) traffic gets the lowest share of
bandwidth. This in turn ensures that the highest priority packets are delivered with minimum
delay meeting the critical data delivery requirements.
124
Figure 2. Bandwidth allocations for different priority data for 256 nodes

125
Figures 3, 4, 5 indicate the results of bandwidth allocation with 128, 64 and 32 nodes
respectively. The simulation results with 128, 64 and 32 nodes are consistent with the result of
256 nodes. The percentage of bandwidth allocated for higher priority data is higher. The critical
data which is event driven data [22], can be delivered with minimum delay meeting the
requirements of the application.

126
Figure 6. End-to-End Delay
In figure 6 we are comparing the end-to end delay of packet delivery in our method with the
end-to end delay incurred in transmission of data packets with the AODV [20] protocol for
priority 0, the critical data. AODV is an on-demand routing protocol which is widely used for
wireless networks. This protocol has less routing and computational overheads, simplicity, and
good scalability. But the protocol does not consider QoS or reliability as demanded by the traffic
[21]. AODV does not differentiate the packets with different priority. AODV applies random
packet scheduling [23]. The end-to end delay incurred in our protocol is less than that of AODV
protocol as indicated by the graph in figure 6. The real time critical data is assigned the highest
share of bandwith and hence is delivered with minimum dealy.
5. CONCLUSION
This paper details the priority based data delivery to the sink node from the sensor nodes. In this
work, we consider the bandwidth allocation based on the priority of the data for the applications
where sensor nodes are deployed in Ad-hoc manner to detect critical events. The data generated
in WSN has different priority levels. WSNs are application specific and several applications of
WSN are designed for vital event monitoring and to ensure timeliness and reliability for the
measured environmental values [13]. The real time data has to be prioritized based on the urgency
and resources such as bandwidth needs to be allocated accordingly to ensure the timely delivery
of the data to the sink node. The bandwidth allocation for each flow depends on the QoS
requirement of the specific flow [24]. The MAC layer implements priority based medium access,
and queuing mechanism, which ensures the critical data delivery in a prioritized manner with
bandwidth partitioning among different priority transmissions. We have applied the discrete
event simulator (NS-2) to model the behavior of the system. Simulation has been conducted for
different node distributions. The results are compared with similar known technique. Our
technique reduces end-to end delay in data delivery as depicted in figure 6. Future research
direction of this work could be an enhancement to support load balancing. We have considered a
static base station and nodes in this work. Studies may be further extended for scenarios
considering the mobility of the sink, the sensor nodes or both.

127
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AUTHORS
Mary Cherian has 30 years of experience in professional field spread over education,
research and industry. She holds B.E degree in electronics and communication from
Kerala University, India and M.Tech. in computer science and engineering from
Visvesvaraya technological university Belgaum, India. She started her career in 1984 as a
research engineer in O/E/N India and has worked in the field of engineering and software
in industries like Kerala state electronics Development Corporation, Keltron controls,
Electronics Research and Development Centre, ABB and Chemtrols Software private
limited in capacities of System Engineer, System Manager and Director. Later, she concentrated on
education and contributed in academic field for the last 15 years in India and abroad educating pupil in the
field of science and technology especially in Computer Science and Engineering. Currently she is working
as Associate Professor in the department of Computer Science and Engineering in Dr. Ambedkar Institute
of Technology, Bangalore, India. She has publications in national and international conferences and
journals. Her areas of interests include Computer networks, Sensor Networks, Real time routing protocols,
and Cognitive routing. Prof Cherian has membership of professional bodies such as CSI, ISTE, IE and
IETE.
Dr. T. R. Gopalakrishnan Nair has 35 years of experience in professional field spread over
education, research and industry. He holds degrees M.Tech. from I.I.Sc., Bangalore, India
and Ph.D. in computer Science, from Kerala University, India. He started his career in
Electronics Research and Development Centre, Trivandrum, India where he was
instrumental in developing various pioneering research products in the field of computers
and software. Later, as the head of Advanced Simulation Activities in Indian Space
Research Organization, his areas of research were Critical Real-Time Systems, Inertial Navigation and
Guidance systems, Launch Vehicle Technology, High speed computing and Launch Vehicle simulations.
Later, he concentrated on education and contributed extensively in academic field for the last 14 years in
India and abroad educating pupil in the field of science and technology especially in Computer Science and
Engineering. He authored and published about 110 papers in these multidisciplinary fields and he promotes
cross domain fusion of knowledge. He has authored several book chapters at international levels and
delivered keynote and invited lectures. He is the Chief Editor of Journals "Inter JRI Science and
Technology and Inter JRI Computer Science and Networking” published by Interline Publishers. In 1992,
he received the National Technology award PARAM from the advisor to Prime Minister, for developing
the parallel computing flight simulation systems. He received the Team Tech Foundation Award for
'Excellence in Education and Research' in 2009. Dr. Nair is a senior member of IEEE for last two decades
and a member of various other societies like ACM. His areas of interests include Computer networks,
Cognitive routing, Software Engineering, Bio-Informatics, AI & Robotics and Signal and Image
Processing.

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